| commit | 82611a987b616f0b47c8ac1e340ee94c7919c947 | [log] [tgz] |
|---|---|---|
| author | Ben Vanik <ben.vanik@gmail.com> | Wed Oct 18 16:57:45 2023 -0700 |
| committer | GitHub <noreply@github.com> | Wed Oct 18 16:57:45 2023 -0700 |
| tree | b7c59efbcabc0648762e127d54e4514308474184 | |
| parent | cda49ca393cfe2d9e151e273015971eaac6171a9 [diff] |
Making execution region results queue-ordered allocas. (#15149) We don't currently insert deallocas and don't track live ranges but that can come in the future as we support more control flow. For now this at least gets all of the common allocations within an invocation into the queue-ordered bucket so that we can do proper async execution and use native queue-ordered (e.g. stream-ordered allocations in CUDA) functionality. With this change the caching allocator is no longer needed for CUDA in almost all cases (besides exported function results).
IREE (Intermediate Representation Execution Environment, pronounced as “eerie”) is an MLIR-based end-to-end compiler and runtime that lowers Machine Learning (ML) models to a unified IR that scales up to meet the needs of the datacenter and down to satisfy the constraints and special considerations of mobile and edge deployments.
See our website for project details, user guides, and instructions on building from source.
IREE is still in its early phase. We have settled down on the overarching infrastructure and are actively improving various software components as well as project logistics. It is still quite far from ready for everyday use and is made available without any support at the moment. With that said, we welcome any kind of feedback on any communication channels!
See our website for more information.
IREE is licensed under the terms of the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.